1 / 39

Computer Vision – Introduction

Computer Vision – Introduction. Hanyang University Jong-Il Park. This Class. Goal Understanding basic concepts and methodologies for image processing(IP) Developing a foundation for further study and research in IP and computer vision(CV) Learning how to develop IP/CV software How?

cdoucette
Télécharger la présentation

Computer Vision – Introduction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Computer Vision – Introduction Hanyang University Jong-Il Park

  2. This Class • Goal • Understanding basic concepts and methodologies for image processing(IP) • Developing a foundation for further study and research in IP and computer vision(CV) • Learning how to develop IP/CV software • How? • Lectures: 3 hours/week • Ordinary lecture(3/4) • Programming lesson(1/4) • Assignments: 2~3/month

  3. Textbook • Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 3rd Edition, Prentice Hall, 2002. • References • Inoue et al., C언어로 배우는 실천 영상처리, 성안당, 2003. • 황선규, 영상처리프로그래밍 by Visual C++, 한빛미디어, 2007.

  4. Background 1. Probability Theory and Random Process 2. Linear Algebra 3. Signals and Systems 4. Digital Signal Processing 5. C/C++ programming skill

  5. Images

  6. What is Image? • picture size  picture resolution ; 256256, 512512 • 0  f(x,y)  L(=255) ; gray level, 8bit/pixel • (x,y) ;spatial coordinate • t ;temporal coordinate

  7. Image formation

  8. [http://learn.hamamatsu.com/articles/microscopyimaging.html]

  9. Image Coordinate

  10. ; M×N matrix ;aij= f (x=i, y=j)

  11. Spatial Resolution

  12. Gray-Level Resolution 256 128 16 8 64 32 4 2

  13. [http://learn.hamamatsu.com/articles/microscopyimaging.html]

  14. Functional form • Functional forms • f(x,y) : 2-D still image • f(x,y,t), f(x,y,z) : video sequence, 3D object • f(x,y,z,t) : moving 3-D object • Meaning • brightness(luminance) or color of an object • TV camera, scanner • absorption characteristics of objects(especially bodies) • X-ray imaging, Ultrasonic imaging, CT • distance between objects and measuring instrument • sonar imaging, radar imaging, range camera • temperature of an object • IR(infrared) camera

  15. Famous Images Barboon (512*512) Boat (512*512) Lena (512*512)

  16. First photograph due to Niepce, First on record shown - 1822 Basic abstraction is the pinhole camera First successful commercial photograph due to Eastman in late 19th First photograph

  17. First digital picture

  18. First digital image processing • Early 1960s

  19. DIP in medical imaging • Late 1960s and early 1970s • Computed tomography(CT) Recent Application NIKS Hanyang Univ. 2002.

  20. EM spectrum

  21. Examples:Gamma-ray imaging

  22. Examples:X-ray imaging

  23. Examples:Microscopy images

  24. Thematic bands

  25. Examples:Multi-spectral imaging

  26. Examples:Imaging in the visible band

  27. Examples:Infrared imaging Nighttime Lights of The World

  28. Examples:Microwave imaging Radar image

  29. Examples:Radio band imaging

  30. Examples:Ultrasound imaging

  31. Examples:Electron microscope Scanning Electron Microscope

  32. Examples:Computer-generated images

  33. IP vs. Computer Vision Vision continuum Image processing Image analysis Computer vision Mid-level High-level Low-level • Filtering • Enhancement • Restoration • Edge detection • Compression • Segmentation • Classification • Recognition • AI Image-in Image-out Image-in Feature-out Image-in Decision-out

More Related